pylimma.MArrayLM

class pylimma.MArrayLM(data=None, /, **kwargs)

Bases: _LargeDataObject

Linear-model-fit container (Python equivalent of R limma’s MArrayLM).

Holds the output of lm_fit / contrasts_fit / e_bayes / treat.

__init__(data=None, /, **kwargs)

Methods

__init__([data])
as_dataframe([row_names])	Flatten the fit into a pandas.DataFrame with one row per probe.
clear()
copy()
dim()
fitted()	Fitted values coefficients @ design.T.
fromkeys(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
head([n])
items()
keys()
pop(k[,d])	If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem()	Remove and return a (key, value) pair as a 2-tuple.
residuals(y)	Residuals y - fitted.
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
tail([n])
update([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()

Attributes

dimnames
ncol
nrow
shape

as_dataframe(row_names=None)

Flatten the fit into a pandas.DataFrame with one row per probe. Port of R limma’s as.data.frame.MArrayLM (classes.R). Only slots whose first dimension matches the number of probes are retained.

Return type:

DataFrame

fitted()

Fitted values coefficients @ design.T. Port of R limma’s fitted.MArrayLM (lmfit.R). Raises when the fit holds contrasts rather than raw coefficients.

Return type:

ndarray

residuals(y)

Residuals y - fitted. Port of R limma’s residuals.MArrayLM (lmfit.R).

Return type:

ndarray